Given the widespread availability and usage of the internet by consumers, many businesses have become interested in being able to effectively monitor the content and commentary provided by such consumers. Interactive websites such as social networks and blogs provide a wealth of useful information that can be advantageously used by a business. It would be very desirable to allow the businesses to stay informed of actionable social networking content, for example, to identify potential customers and possible sales leads or to identify problematic situations that may require immediate involvement of customer service personnel.
With many forms of social media, the content of the social media message is itself often sufficient to allow recognition of topic of that content. This is because the social media content will often include a large enough quantity of data to make it readily apparent what that content is directed towards. For example, a blog posting will often include a large and detailed quantity of text and/or pictures that make the topic of that blog posting very self-evident.
However, there are many types of social media content where it is very commonplace to have very small quantities of content for each posting. For example, there are many types of systems that allow for sharing electronic messages among a community of users, where the content of each message may only have a few words, phrases, or sentences. Twitter is a notable example of this type of message sharing system where each message may only contain a very small snippet of text. Other examples of message systems that may include very small message snippets include Internet forums, electronic mailing lists, blogs and microblogs, and social networks. In any of these systems, users may post very brief messages that can be read by other users of the system.
With these types of messages, it is very difficult by just looking at the message itself to determine the topic of the message. This creates a problem for any electronic system that seeks to perform automated analysis of the social media content.
Various techniques have been implemented in an attempt to address this problem. For example, hash tags are often used to provide the context for a particular message or tweet. An electronic analysis system can use the hash tags to interpret the content or topic of the message, even if there is not a sufficient quantity of data in the message itself to permit this type of analysis.
However, this approach suffers from many drawbacks. First, this technique is useless if the user creating the message fails to use hash tags. Even when used, problems occur if the message creators use inconsistent hash tags, or if a mistake is made in the hash tag, e.g., when a typographical or spelling error occurs in the hash tag.
Therefore, there is a need for an improved approach that can be used to analyze any social media content, even social media content that contain very small quantities of data.